openai_query

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It usefully discloses the underlying API (OpenAI Responses API), the web_search tool usage, the default model environment variable, and the return shape. However, it does not mention API key requirements, rate limits, cost implications, or error behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is brief, front-loaded with the core purpose, and every sentence adds useful context (return shape, underlying API, default model). There is no redundant or wasted text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need not be explained further. However, with no annotations and 0% schema description coverage, the description should cover the 'system_prompt' parameter and basic operational expectations like API key or rate-limit behavior; those gaps keep it from being complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must explain all three parameters. It partially covers the 'question' parameter and mentions the default model, but 'system_prompt' is never explained and the 'model' parameter is only partially addressed via the default-model note.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action ('Ask ChatGPT'), the resource (a question), and the return shape (answer + cited URLs). The engine name distinguishes it from sibling LLM query tools like claude_query or gemini_query, but it does not explicitly contrast itself with multi_llm_query or list_llm_engines.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies this is for OpenAI/ChatGPT queries with web search, but gives no explicit guidance on when to use it versus the many sibling chat/LLM query tools. There are no stated exclusions or alternative-routing hints.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.